1. Field of Invention
The present invention relates to a music recommendation method. More particularly, the present invention relates to a music recommendation method for mining a user's preferable perceptual patterns from music pieces.
2. Description of Related Art
Recent advances in music compression technologies have eased the access of music pieces. Through the modern communication tools, a user may purchase music items, such as songs, from online e-commerce stores, such as Amazon, Flickr, Google, and Youtube, without visiting the physical music stores in person. However, it is not easy for the user to identify what her/his favorite music items are from a huge amount of available music pieces. This enables a large increase in the number of music recommender systems. In conventional recommender systems, the user's preference is represented by using a rating scale of one to five. Based on the rating scale, the user's preference and the music items can be bridged reasonably by machine-learning techniques, thereby predicting the ratings of un-purchased music items for a user, thereupon deriving the ranking list of the un-purchased items.
Collaborative filtering (CF) is a typical recommendation paradigm, and the basic assumption behind the CF is that, if users conduct similar behaviors on rating music items, they have correlated interests on the music items. That is, the users with similar rating behaviors are always grouped together to assist each other in making a selection decision among a number of music items. Mostly, CF has been shown to be effective on predicting users' preferences. However, CF-based methods still incur a rating diversity problem, meaning that similar ratings fail to represent the user's preferences on the contents of the musical items precisely. On one hand, two different kinds of music items could be similar on having high rating coefficients. On the other hand, the ratings of one specific music item could be diverse extremely. Whatever it is, it is not east to derive the correct recommendation result merely by users' ratings.
Hence, there is a need to provide a music recommendation method for overcoming the problem of rating diversity described above.